A Surrogate Approach to Explainable AI for Predictive Maintenance: Techniques and Applications

Predictive maintenance is a process that utilizes machine-learning models to predict equipment failure or maintenance needs, thus enabling proactive interventions and optimizing operational costs. However, these models often lack transparency and act as black boxes that provide predictions without explanation. Trust, safety, and compliance are hampered by this opacity, particularly in vital applications like manufacturing, aviation, and energy. In order to overcome this difficulty, explainable AI (XAI) approaches improve the interpretability and transparency of machine-learning algorithms and offer insights into how they make decisions. This paper presents a comprehensive framework for applying XAI to predictive maintenance, and explores various techniques and their applications. The techniques investigated included feature importance, model interpretability, visual explanations, and counterfactual explanations. These techniques were evaluated on a real-world dataset, demonstrating their effectiveness in interpreting predictions, identifying influential factors, and providing actionable insights into maintenance decisions. The tradeoffs between interpretability and model accuracy are also covered in this work, along with the difficulties and potential paths for further research in this area.